Tom Greene

A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation

The glomerular filtration rate (GFR) is traditionally considered the best overall index of renal function in health and disease (1). Because GFR is difficult to measure in clinical practice, most clinicians estimate the GFR from the serum creatinine concentration. However, the accuracy of this estimate is limited because the serum creatinine concentration is affected by factors other than creatinine filtration (2, 3). To circumvent these limitations, several formulas have been developed to estimate creatinine clearance from serum creatinine concentration, age, sex, and body size (4-12). Despite more recent studies that have related serum creatinine concentration to GFR (13-24), no formula is more widely used to predict creatinine clearance than that proposed by Cockcroft and Gault (4). This formula is used to detect the onset of renal insufficiency, to adjust the dose of drugs excreted by the kidney, and to evaluate the effectiveness of therapy for progressive renal disease. More recently, it has been used to document eligibility for reimbursement from the Medicare End Stage Renal Disease Program (25) and for accrual of points for patients on the waiting list for cadaveric renal transplantation (26). Major clinical decisions in general medicine, geriatrics, and oncology (as well as nephrology) are made by using the Cockcroft-Gault formula and other formulas to predict the level of renal function. Therefore, these formulas must predict GFR as accurately as possible. The Modification of Diet in Renal Disease (MDRD) Study, a multicenter, controlled trial, evaluated the effect of dietary protein restriction and strict blood pressure control on the progression of renal disease (27-30). During the baseline period, GFR, serum creatinine, and several variables that affect the relation between them were measured in patients with chronic renal disease. The purpose of our study was to develop an equation from MDRD Study data that could improve the prediction of GFR from serum creatinine concentration. Methods Baseline Cohort and Measurement Methods in the Modification of Diet in Renal Disease Study The overall study design and methods of recruitment for the MDRD Study have been described elsewhere (31, 32). A total of 1785 patients entered the baseline period. Of these patients, 1628 (91%) also underwent measurement of GFR and the other variables described below; these patients constitute the study group for these analyses. Glomerular filtration rate was measured as the renal clearance of 125I-iothalamate (33, 34). Creatinine clearance was computed from creatinine excretion in a 24-hour urine collection and a single measurement of serum creatinine. Serum and urine creatinine were measured by using a kinetic alkaline picrate assay with a normal range in serum of 62 to 124 mol/L (0.7 to 1.4 mg/dL) (35). Glomerular filtration rate and creatinine clearance were expressed per 1.73 m2 of body surface area by multiplying measured values by 1.73/body surface area (36). The serum and urine specimens were also used for other measurements, including serum albumin (bromcresol green method [35]), serum urea nitrogen (urease method [35]), and urine urea nitrogen (urease method [35]). Protein intake (g/d) was estimated as 6.25 [UUN (g/d) + 0.031 (g/kg per day) SBW (kg)], where UUN is urine urea nitrogen, SBW is standard body weight, and 0.031 g/kg per day is a constant reflecting the rate of excretion of nitrogen in compounds other than urine urea (37, 38). The diagnosis of diabetes and the cause of renal disease were assigned on the basis of chart review at the clinical center (39). Statistical Analysis Descriptive Statistics The relation of renal function measurements to other baseline characteristics was assessed by using contingency tables, t-tests, analysis of variance, and linear regression, as appropriate. Nonparametric tests (Wilcoxon rank-sum tests and Kruskal-Wallis tests) gave consistent results. A P value less than 0.01 was considered statistically significant. Multivariable Analysis of Glomerular Filtration Rate We used stepwise multiple regression to determine a set of variables that jointly predicted GFR. The stepwise regression models were developed by using a training sample consisting of a random sample of 1070 of the 1628 patients. We found that the variability of the difference between the observed and predicted GFR values was greater for higher GFR values. This increase was eliminated by performing multiple regressions on log-transformed data. To facilitate clinical interpretation, the results were re-expressed in terms of the original units. Consequently, the prediction equation is a multiplicative model; regression coefficients refer to the change in geometric mean GFR associated with unit changes in the independent variable. Predicted GFR is expressed in mL/min per 1.73 m2. The following variables were considered for possible inclusion in the regression model: weight, height, sex, ethnicity, age, diagnosis of diabetes, serum creatinine concentration, serum urea nitrogen level, serum albumin level, serum phosphorus level, serum calcium level, mean arterial pressure, urine creatinine level, urine urea nitrogen level, urine protein level, and urine phosphorus level. The cause of renal disease was not included because in clinical practice, the cause may be unknown or clinicians may not use the same classification method as the investigators in the MDRD Study. A P value less than 0.001 was used as the criterion for entry of a variable into the model. Because of the difficulty in collecting complete 24-hour urine samples in clinical practice, an additional stepwise regression was performed to develop a prediction model that did not include urine biochemistry variables. Finally, because of the interest in developing a prediction equation to assess eligibility for Medicare reimbursement and listing for cadaveric renal transplantation, we repeated the analysis restricting the population to the subgroup of patients with higher serum creatinine concentrations (>221 mol/L [2.5 mg/dL]; n=509 in the training sample). Methods for Comparing Equations To Predict Glomerular Filtration Rate We first developed coefficients for each prediction equation (including the selection of the predictor variables for the stepwise regressions) using the data from the training sample to predict log GFR. Each prediction equation also included a multiplicative constant to account for any consistent bias in the application of that equation in the MDRD Study Group. This was particularly important for equations that are intended to estimate creatinine clearance, which is known to be higher than GFR. The regression coefficients determined in the training sample were then applied to obtain predicted GFRs in a separate validation sample consisting of the remaining 558 patients (172 patients with serum creatinine concentration>221 mol/L [2.5 mg/dL]). These predicted GFR values were compared with the actual GFRs in the validation sample to evaluate the performance of each prediction equation. In this way, separate data sets were used to construct the equations and assess their accuracy after removal of systematic bias. For each equation, we computed overall R 2 (percentage of variability in log GFR explained by the regression model) and the 50th, 75th, and 90th percentiles of the distribution of the percentage absolute difference between measured and predicted GFRs in the validation sample. The 50th percentiles indicate the typical size of the errors in prediction of GFR, and the 75th and 90th percentiles assess the sizes of the larger errors that occurred for each model. Development of Final Prediction Equations To improve the accuracy of the final MDRD Study prediction equations, the regression coefficients derived from the training sample were updated on the basis of data from all 1628 patients. As a result, the standard errors of the regression coefficients in the final MDRD Study prediction equations are slightly smaller than those derived from the training sample; thus, the accuracy of the final prediction equations may be slightly better (by about 0.1% to 0.2%) than their accuracy as assessed in the validation sample. Results Demographic and Clinical Characteristics The mean age ( SD) of the cohort was 50.6 12.7 years. Sixty percent of patients were male, 88% were white, and 6% were diabetic. Causes of renal disease were glomerular disease (32%), polycystic kidney disease (22%), tubulointerstitial disease (7%), and other or unknown renal diseases (40%). Mean protein intake was 0.99 0.24 g/kg of body weight per day and mean arterial pressure was 99.4 12.2 mm Hg. Mean weight was 79.6 16.8 kg, body surface area was 1.91 0.23 m2, serum urea nitrogen concentration was 11.4 5.7 mmol/L [32 16 mg/dL], and serum albumin concentration was 40.0 4.0 g/L [4.0 0.4 g/dL], respectively. Glomerular Filtration Rate, Creatinine Clearance, and Serum Creatinine Concentration Renal function measurements for the study group and for various subgroups are shown in Table 1. Mean GFR for the population was 0.38 mL s 2 m 2 (39.8 mL/min per 1.73 m2), with lower values in patients with lower protein intake, white patients compared with black patients, and older patients ( 55 years) compared with younger patients (P<0.01). The mean value of creatinine clearance was 0.81 mL s 2 m 2 (48.6 mL/min per 1.73 m2) and was lower in older patients and patients with lower protein intake (P 0.01). The mean serum creatinine concentration was 203 mol/L (2.3 mg/dL) and was higher in men, patients with lower protein intake, and patients with higher mean arterial pressure (P 0.01). Figure 1 shows the well-known reciprocal relation of serum creatinine concentration to GFR for subgroups based on sex and ethnicity. At any given GFR, the serum creatinine concentration is significantly higher in men than in women and in black persons than in white persons (P<0.001). Table 1. Association of Renal Fu

Using Standardized Serum Creatinine Values in the Modification of Diet in Renal Disease Study Equation for Estimating Glomerular Filtration Rate

Context Guidelines recommend that laboratories estimate glomerular filtration rate (GFR) with equations that use serum creatinine level, age, sex, and ethnicity. Standardizing creatinine measurements across clinical laboratories should reduce variability in estimated GFR. Contribution Using standardized creatinine assays, the authors calibrated serum creatinine levels in 1628 patients whose GFR had been measured by urinary clearance of 125I-iothalamate. They used these data to derive new equations for estimating GFR and to measure their accuracy. The equations were inaccurate only when kidney function was near-normal. Cautions There was no independent sample of patients for measuring accuracy. Implications By using this equation and a standardized creatinine assay, different laboratories can report estimated GFR more uniformly and accurately. The Editors Chronic kidney disease is a recently recognized public health problem. Current guidelines define chronic kidney disease as kidney damage or a glomerular filtration rate (GFR) less than 60 mL/min per 1.73 m2 for 3 months or more, regardless of cause (13). Kidney damage is usually ascertained from markers, such as albuminuria. The GFR can be estimated from serum creatinine concentration and demographic and clinical variables, such as age, sex, ethnicity, and body size. The normal mean value for GFR in healthy young men and women is approximately 130 mL/min per 1.73 m2 and 120 mL/min per 1.73 m2, respectively, and declines by approximately 1 mL/min per 1.73 m2 per year after 40 years of age (4). To facilitate detection of chronic kidney disease, guidelines recommend that clinical laboratories compute and report estimated GFR by using estimating equations, such as equations derived from the Modification of Diet in Renal Disease (MDRD) Study (13, 510). The original MDRD Study equation was developed by using 1628 patients with predominantly nondiabetic kidney disease. It was based on 6 variables: age; sex; ethnicity; and serum levels of creatinine, urea, and albumin (11). Subsequently, a 4-variable equation consisting of age, sex, ethnicity, and serum creatinine levels was proposed to simplify clinical use (3, 12). This equation is now widely accepted, and many clinical laboratories are using it to report GFR estimates. Extensive evaluation of the MDRD Study equation shows good performance in populations with lower levels of GFR but variable performance in those with higher levels (1332). Variability among clinical laboratories in calibration of serum creatinine assays (33, 34) introduces error in GFR estimates, especially at high levels of GFR (35), and may account in part for the poorer performance in this range (13, 14, 16, 1821, 27, 30). The National Kidney Disease Education Program (NKDEP) has initiated a creatinine standardization program to improve and normalize serum creatinine results used in estimating equations (36). The MDRD Study equation has now been reexpressed for use with a standardized serum creatinine assay (37), allowing GFR estimates to be reported in clinical practice by using standardized serum creatinine and overcoming this limitation to the current use of GFR estimating equations. The purpose of this report is to describe the performance of the reexpressed 4-variable MDRD Study equation and compare it with the performance of the reexpressed 6-variable MDRD equation and the CockcroftGault equation (38), with particular attention to the level of GFR. This information should facilitate implementation of reporting and interpreting estimated GFR in clinical practice. Methods Laboratory Methods Urinary clearances of 125I-iothalamate after subcutaneous infusion were determined at clinical centers participating in the MDRD Study. Serum and urine 125I-iothalamate were assayed in a central laboratory. All serum creatinine values reported in this study are traceable to primary reference material at the National Institute of Standards and Technology (NIST), with assigned values based on isotope-dilution mass spectrometry. The serum creatinine samples from the MDRD Study were originally assayed from 1988 to 1994 in a central laboratory with the Beckman Synchron CX3 (Global Medical Instrumentation, Inc., Ramsey, Minnesota) by using a kinetic alkaline picrate method. Samples were reassayed in 2004 with the same instrument. The Beckman assay was calibrated to the Roche/Hitachi P module Creatinase Plus enzymatic assay (Roche Diagnostics, Basel, Switzerland), traceable to an isotope-dilution mass spectrometry assay at NIST (37, 39). On the basis of these results, the 4-variable and 6-variable MDRD Study equations were reexpressed for use with standardized serum creatinine assay. The CockcroftGault equation was not reexpressed because the original serum creatinine samples were not available for calibration to standardized serum creatinine assay. Derivation and Validation of the MDRD Study Equation The MDRD Study was a multicenter, randomized clinical trial of the effects of reduced dietary protein intake and strict blood pressure control on the progression of chronic kidney disease (40). The derivation of the MDRD Study equation has been described previously (11). Briefly, the equation was developed from data from 1628 patients enrolled during the baseline period. The GFR was computed as urinary clearance of 125I-iothalamate. Creatinine clearance was computed from creatinine excretion in a 24-hour urine collection and a single measurement of serum creatinine. Glomerular filtration rate and creatinine clearance were expressed per 1.73 m2 of body surface area. Ethnicity was assigned by study personnel, without explicit criteria, probably by examination of skin color. The MDRD Study equation was developed by using multiple linear regression to determine a set of variables that jointly estimated GFR in a random sample of 1070 patients (development data set). The regressions were performed on log-transformed data to reduce variability in differences between estimated and measured GFR at higher levels. Several equations were developed, and the performance of these equations was compared in the remaining sample of 558 patients (validation data set). To improve the accuracy of the final equations, the regression coefficients derived from the development data set were updated on the basis of data from all 1628 patients (11). Estimation of GFR Glomerular filtration rate was estimated by using the following 4 equations: the reexpressed 4-variable MDRD Study equation (GFR= 175standardized Scr 1.154age0.2031.212 [if black]0.742 [if female]), the reexpressed 6-variable MDRD Study equation (GFR= 161.5standardized Scr 0.999age0.176SUN0.17albumin0.3181.18 [if black]0.762 [if female]), the CockcroftGault equation adjusted for body surface area (Ccr= [140age]weight0.85 [if female]1.73/72 standardized ScrBSA), and the CockcroftGault equation adjusted for body surface area and corrected for the bias in the MDRD Study sample (Ccr= 0.8[140age]weight0.85 [if female]1.73/72 standardized ScrBSA). In these equations, GFR and creatinine clearance (Ccr) are expressed as mL/min per 1.73 m2, serum creatinine and urea nitrogen (SUN) are expressed as mg/dL, albumin is expressed as g/dL, weight is expressed as kg, age is expressed as years, and body surface area (BSA) is expressed as m2. Correction for bias improves performance of the CockcroftGault equation because it adjusts for systematic differences between studies, such as differences in the measures of kidney function (GFR in the MDRD Study and creatinine clearance in the study by Cockcroft and Gault), the serum creatinine assays, and the study samples. Hence, the bias correction for the CockcroftGault equation provided here reexpresses that equation for the estimation of GFR for use with standardized creatinine in study samples similar to that in the MDRD Study. Measures of Performance Measures of performance include bias (median difference of measured minus estimated GFR and measured GFR) and percentage bias (percentage of bias divided by measured GFR), precision (interquartile range of the difference between estimated and measured GFR, and percentage of variance in log-measured GFR explained by the regression model [R2 values]), and accuracy (percentage of estimates within 30% of the measured values). In the overall data set, bias is expected to be close to 0 for equations derived in the MDRD Study database, including the 4-variable and 6-variable equations and the CockcroftGault equation adjusted for bias. The bootstrap method (based on percentiles, with 2000 bootstrap samples) was used to estimate 95% CIs for interquartile ranges and R2 values. Confidence intervals for the percentage of estimates within 30% of measured values were computed by using the normal approximation to the binomial or exact binomial probabilities, as appropriate. We also computed sensitivity, specificity, positive and negative predictive value of estimated GFR less than 60 mL/min per 1.73 m2, and receiver-operating characteristic (ROC) curves by using measured GFR less than 60 mL/min per 1.73 m2 as the criterion standard. Areas under the ROC curves were compared by using the method of DeLong and colleagues (41). R, version 2 (Free Software Foundation, Inc., Boston, Massachusetts), and SAS, version 9.1 (SAS Institute, Inc., Cary, North Carolina), were used for statistical analysis. We used the lowess function in R to plot smoothed functions in the figures. Role of the Funding Source The study was funded by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) as part of a cooperative agreement that gives the NIDDK substantial involvement in the design of the study and in the collection, analysis, and interpretation of the data. The NIDDK was not required to approve publication of the finished manuscript. The institutional review boards of all participating institutions approved the study. Results Clinical characteristics of

Low-Level Environmental Lead Exposure and Children's Intellectual Function: An International Pooled Analysis

Lead is a confirmed neurotoxin, but questions remain about lead-associated intellectual deficits at blood lead levels < 10 μg/dL and whether lower exposures are, for a given change in exposure, associated with greater deficits. The objective of this study was to examine the association of intelligence test scores and blood lead concentration, especially for children who had maximal measured blood lead levels < 10 μg/dL. We examined data collected from 1,333 children who participated in seven international population-based longitudinal cohort studies, followed from birth or infancy until 5–10 years of age. The full-scale IQ score was the primary outcome measure. The geometric mean blood lead concentration of the children peaked at 17.8 μg/dL and declined to 9.4 μg/dL by 5–7 years of age; 244 (18%) children had a maximal blood lead concentration < 10 μg/dL, and 103 (8%) had a maximal blood lead concentration < 7.5 μg/dL. After adjustment for covariates, we found an inverse relationship between blood lead concentration and IQ score. Using a log-linear model, we found a 6.9 IQ point decrement [95% confidence interval (CI), 4.2–9.4] associated with an increase in concurrent blood lead levels from 2.4 to 30 μg/dL. The estimated IQ point decrements associated with an increase in blood lead from 2.4 to 10 μg/dL, 10 to 20 μg/dL, and 20 to 30 μg/dL were 3.9 (95% CI, 2.4–5.3), 1.9 (95% CI, 1.2–2.6), and 1.1 (95% CI, 0.7–1.5), respectively. For a given increase in blood lead, the lead-associated intellectual decrement for children with a maximal blood lead level < 7.5 μg/dL was significantly greater than that observed for those with a maximal blood lead level ≥7.5 μg/dL (p = 0.015). We conclude that environmental lead exposure in children who have maximal blood lead levels < 7.5 μg/dL is associated with intellectual deficits.

Blood Pressure Control, Proteinuria, and the Progression of Renal Disease: The Modification of Diet in Renal Disease Study

Progressive functional deterioration occurs in most forms of chronic renal disease [1, 2]. Although the mechanisms underlying the progression of renal disease are probably multifactorial [2, 3], both hypertension [3-5] and proteinuria [3, 6-9] may contribute to the progressive loss of renal function. The Modification of Diet in Renal Disease (MDRD) Study compared the rates of decline in glomerular filtration rate in 840 patients with a diverse array of renal diseases who were randomly assigned to either a usual or a low blood pressure goal [10, 11]. In study A (baseline glomerular filtration rate, 25 to 55 mL/min1.73 m2), the intent-to-treat analysis included all patients and showed that the mean decline in glomerular filtration rate was faster in the first 4 months of follow-up and slower thereafter in the low than in the usual blood pressure group [10]. These results suggest that the low blood pressure goal has a long-term benefit. However, the duration of follow-up (0 to 3.7 years) was insufficient to show a difference between the two blood pressure groups in the decline of glomerular filtration rate at the end of the study. In study B (baseline glomerular filtration rate, 13 to 24 mL/min1.73 m2), the decline in glomerular filtration rate was linear and did not differ significantly between the two blood pressure groups. In studies A and B, subgroup analyses showed that baseline proteinuria was a strong predictor of subsequent decline in glomerular filtration rate and that assignment to the low blood pressure goal produced a significantly greater benefit on the rate of decline in glomerular filtration rate in patients with higher baseline proteinuria [10, 12]. We examined the relation of prescribed and achieved blood pressure with decline in glomerular filtration rate, placing particular emphasis on how this relation is affected by proteinuria at baseline. We also evaluated the effect of blood pressure on changes in proteinuria and the relation between changes in proteinuria during follow-up and subsequent decline in glomerular filtration rate. Methods Study Design The MDRD Study consisted of two randomized clinical trials done in a total of 840 patients who had chronic renal diseases of diverse cause [10, 11]. The study protocol was approved by the review boards of the 15 participating institutions. Eligibility criteria included the following: age of 18 to 70 years; serum creatinine level of 1.2 to 7.0 mg/dL for women and 1.4 to 7.0 mg/dL for men or a creatinine clearance less than 70 mL/min1.73 m2; and mean arterial pressure of 125 mm Hg or less [13]. Patients were excluded if they had diabetes requiring insulin, proteinuria of 10 g/d or more, or body weight less than 80% or more than 160% of standard body weight [14]. After a 3-month baseline period, 585 patients who had a glomerular filtration rate of 25 to 55 mL/min1.73 m2, dietary protein intake of more than 0.9 g/kgd, and mean arterial pressure of 125 mm Hg or less were entered into study A. Two hundred fifty-five patients who had a glomerular filtration rate of 13 to 24 mL/min1.73 m2 and a mean arterial pressure of 125 mm Hg or less were entered into study B. Patients in both studies were randomly assigned to either a usual or a low blood pressure goal. The usual blood pressure goal was a mean arterial pressure of 107 mm Hg or less (for patients less than equals 60 years of age) or 113 mm Hg or less (for patients more than equals 61 years of age). The low blood pressure goal was 15 mm Hg lower: It was a mean arterial pressure of 92 mm Hg or less (for patients less than equals 60 years of age) or 98 mm Hg or less (for patients more than equals 61 years of age). Patients in study A were also randomly assigned to receive either a usual protein (1.3 g/kgd) or low-protein (0.58 g/kgd) diet. Patients in study B were assigned to receive either the low-protein or a very-low-protein (0.28 g/kgd) diet supplemented with a mixture of ketoacids and amino acids (0.28 g/kgd; Ross Laboratories, Columbus. Ohio). Results of the dietary interventions have been reported elsewhere [10, 15]. Randomization, done at the Data Coordinating Center, was stratified according to clinical center and average mean arterial pressure at baseline (both studies A and B) and according to the rate of change in serum creatinine levels before study entry (study A only). Additional details about the baseline period have been reported elsewhere [13, 16]. The mean duration of follow-up was 2.2 years. In study A, 553 patients had glomerular filtration rate measurements extending to at least 1 year of follow-up; 381 patients had glomerular filtration rate measurements extending to at least 2 years of follow-up; and 143 patients had glomerular filtration rate measurements extending to at least 3 years of follow-up. In study B, 219 patients had glomerular filtration rate measurements extending to at least 1 year of follow-up; 137 patients had glomerular filtration rate measurements extending to at least 2 years of follow-up; and 62 patients had glomerular filtration rate measurements extending to at least 3 years of follow-up. Antihypertensive Regimens Both nonpharmacologic and pharmacologic interventions were implemented. During the baseline period, antihypertensive regimens were prescribed to achieve the usual blood pressure goal. After randomization, the regimens were modified to achieve either the low or the usual blood pressure goal. Nonpharmacologic therapy included recommendations for regular exercise and for reductions in body weight and intake of alcohol and sodium. Pharmacologic therapy was based on the stepped-care approach defined in the 1988 Report of the Joint National Committee [17]. Use of all antihypertensive drugs was allowed, but angiotensin-converting enzyme inhibitors, with or without a diuretic, were encouraged as the agents of first choice. Calcium channel blockers, with or without a diuretic, were encouraged as the agents of second choice. Blood pressure was measured monthly using a standardized Hawksley random zero sphygmomanometer (Lancing, United Kingdom). Blood pressure regimens were modified monthly; they were modified more often as was necessary to achieve the blood pressure goals. Measurements and Definitions of Variables The primary outcome variable was decline in glomerular filtration rate. This rate was measured as the renal clearance of 125I-iothalamate [18, 19] at the beginning and end of the baseline period and at 2 months, at 4 months, and at every 4 months thereafter during follow-up. Protein intake was monitored by monthly 24-hour urinary urea nitrogen excretion tests [20]. Blood for hematologic and serum tests was obtained at the beginning and end of the baseline period and every 2 months during follow-up. Renal diagnoses were made using medical records and review of available historical information [13]. Each patient was classified as having one of nine renal diagnoses. If patients had baseline proteinuria of less than 3.0 g/d; had presumptive (rather than established) diagnoses of hypertensive nephrosclerosis, tubulointerstitial diseases, or other diseases; and had not had renal biopsy, they were placed in the other or unknown category. Patients with proteinuria of more than 3.0 g/d who had not had renal biopsy and who had the presumptive diagnoses listed above were placed in the glomerular diseases category. Mean baseline blood pressure was defined as the average of the two mean arterial pressure measurements made at the end of the first and second months of the baseline period. Mean follow-up blood pressure was defined as the average of all mean arterial pressure measurements obtained at nonglomerular filtration rate visits beginning at the third monthly follow-up visit (mean arterial pressure during glomerular filtration rate visits was consistently higher than during other visits). Proteinuria was measured monthly during the baseline period and every 2 months during the follow-up period in 24-hour urine samples by using the trichloroacetic acid (Ponceau) technique [21]. Baseline proteinuria was the average of four measurements. In some analyses, patients were classified into subgroups according to whether their baseline proteinuria was 0 to 0.25 g/d; 0.25 to 1.0 g/d; 1.0 to 3.0 g/d; or 3.0 g/d or more. For the analyses reported here, patients were classified as taking a class of antihypertensive agents if they reported taking agents of that class for more than 50% of follow-up visits. This definition was selected because most patients reported taking agents of any particular class for either less than 25% or more than 75% of follow-up visits. Statistical Analysis Hypothesis tests were considered statistically significant if P < equals 0.05, two-sided. No adjustments were made for multiple comparisons. To eliminate positive skewness, proteinuria was log transformed, and changes in proteinuria were expressed as the percentage change from baseline. Baseline characteristics were compared between groups using t-tests, analysis of variance, or chi-square tests, as appropriate. Comparisons of Randomized Groups Mean protein intake was similar in the usual and low blood pressure groups in both study A and study B. The effect of the blood pressure intervention was similar in the usual and low-protein diet groups in study A and in the low-protein and very-low-protein diet groups in study B. The effect of the dietary intervention was not influenced by baseline proteinuria. Consequently, comparisons of the blood pressure groups included all patients, regardless of dietary assignment. In study A, the decline in glomerular filtration rate in the blood pressure groups was compared using a 2-slope model in which each patient was assumed to have an initial rate of decline in glomerular filtration rate during the first 4 months of follow-up and a possibly different slope thereafter [10]. We used a mixed-effects model that allowed different rates of decline for each pat

Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault Equations in the Estimation of GFR in Health and in Chronic Kidney Disease

The performance of the Modification of Diet in Renal Disease (MDRD) and the Cockcroft-Gault (CG) equations as compared with measured (125)I-iothalamate GFR (iGFR) was analyzed in patients with chronic kidney disease (CKD) and in potential kidney donors. All outpatients (n = 1285) who underwent an iGFR between 1996 and 2003 were considered for analysis. Of these, 828 patients had CKD and 457 were potential kidney donors. Special emphasis was put on the calibration of the serum creatinine measurements. In CKD patients with GFR <60 ml/min per 1.73 m(2), the MDRD equation performed better than the CG formula with respect to bias (-0.5 versus 3.5 ml/min per 1.73 m(2), respectively) and accuracy within 30% (71 versus 60%, respectively) and 50% (89 versus 77%, respectively). Similar results are reported for 249 CKD patients with diabetes. In the kidney donor group, the MDRD equation significantly underestimated the measured GFR when compared with the CG formula, with a bias of -9.0 versus 1.9 ml/min per 1.73 m(2), respectively (P < 0.01), and both the MDRD and CG equations overestimated the strength of the association of GFR with measured serum creatinine. The present data add further validation of the MDRD equation in outpatients with moderate to advanced kidney disease as well as in those with diabetic nephropathy but suggest that its use is problematic in healthy individuals. This study also emphasizes the complexity of laboratory calibration of serum creatinine measurements, a determining factor when estimating GFR in both healthy individuals and CKD patients with preserved GFR.

Serum β-2 Microglobulin Levels Predict Mortality in Dialysis Patients: Results of the HEMO Study

In the randomized Hemodialysis (HEMO) Study, chronic high-flux dialysis, as defined by higher beta-2 microglobulin (beta(2)M) clearance, compared with low-flux dialysis did not significantly alter all-cause mortality in the entire cohort but was associated with lower mortality in long-term dialysis patients. This analysis examined the determinants of serum beta(2)M levels and the associations of serum beta(2)M levels or dialyzer beta(2)M clearance with mortality. In a multivariable regression model that examined 1704 patients, baseline residual kidney urea clearance and dialyzer beta(2)M clearance were strong predictors of predialysis serum beta(2)M levels at 1 mo of follow-up, with regression coefficients of -7.21 (+/-0.69 SE) mg/L per ml/min per 35 L urea volume (P < 0.0001) and -1.94 (+/-0.30) mg/L per ml/min (P < 0.0001),respectively. In addition, black race and baseline years on dialysis correlated positively whereas age, diabetes, serum albumin, and body mass index correlated negatively with serum beta(2)M levels (P < 0.05). In time-dependent Cox regression models, mean cumulative predialysis serum beta(2)M levels but not dialyzer beta(2)M clearance were associated with all-cause mortality (relative risk = 1.11 per 10-mg/L increase in beta(2)M level; 95% confidence interval 1.05 to 1.19; P = 0.001), after adjustment for residual kidney urea clearance and number of prestudy years on dialysis. This association is supportive of the potential value of beta(2)M as a marker to guide chronic hemodialysis therapy.

Effects of dietary protein restriction on the progression of advanced renal disease in the Modification of Diet in Renal Disease Study.

Patients with advanced renal disease randomized to the very low-protein diet group in the Modification of Diet in Renal Disease (MDRD) Study had a marginally (P = 0.066) slower mean glomerular filtration rate (GFR) decline compared with patients randomized to the low-protein diet group. The objective of these secondary analyses was to determine the relationship between achieved, in addition to prescribed, dietary protein intake and the progression of advanced renal disease. A randomized controlled trial was conducted in patients with chronic renal diseases of diverse etiology. The average follow-up was 2.2 years. Fifteen university hospital outpatient nephrology practices participated in the study, which comprised 255 patients aged 18 to 70 years with a baseline GFR 13 to 24 mL/min/1.73 m2 who participated in MDRD Study B. Patients with diabetes requiring insulin were excluded. The patients were given a low-protein (0.58 g/kg/d) or very low-protein (0.28 g/kg/d) diet supplemented with keto acids-amino acids (0.28 g/kg/d). Outcomes were measured by comparisons of protein intake from food or from food and supplement between randomized groups, and correlations of protein intake with rate of decline in GFR and time to renal failure or death. Comparison of the randomized groups showed that total protein intake from food and supplement was lower (P < 0.001) among patients randomized to the very low-protein diet (0.66 g/kg/d) compared with protein intake from food only in patients randomized to the low-protein diet (0.73 g/kg/d). In correlational analyses, we combined patients assigned to both diets and controlled for baseline factors associated with a faster progression of renal disease. A 0.2 g/kg/d lower achieved total protein intake (including food and supplement) was associated with a 1.15 mL/min/yr slower mean decline in GFR (P = 0.011), equivalent to 29% of the mean GFR decline. After adjusting for achieved total protein intake, no independent effect of prescription of the keto acid-amino acid supplement to slow the GFR decline could be detected. If the GFR decline is extrapolated until renal failure, a patient with a 29% reduction in the rate of GFR decline would experience a 41% prolongation in the time to renal failure. Additional analyses confirmed a longer time to renal failure in patients with lower total protein intake. In conclusion, these secondary analyses of the MDRD Study suggest that a lower protein intake, but not the keto acid-amino acid supplement, retards the progression of advanced renal disease. In patients with GFR less than 25 mL/min/1.73 m2, we suggest a prescribed dietary protein intake of 0.6 g/kg/d.

Effects of High-Flux Hemodialysis on Clinical Outcomes: Results of the HEMO Study

Among the 1846 patients in the HEMO Study, chronic high-flux dialysis did not significantly affect the primary outcome of the all-cause mortality (ACM) rate or the main secondary composite outcomes, including the rates of first cardiac hospitalization or ACM, first infectious hospitalization or ACM, first 15% decrease in serum albumin levels or ACM, or all non-vascular access-related hospitalizations. The high-flux intervention, however, seemed to be associated with reduced risks of specific cardiac-related events. The relative risks (RR) for the high-flux arm, compared with the low-flux arm, were 0.80 [95% confidence interval (CI), 0.65 to 0.99] for cardiac death and 0.87 (95% CI, 0.76 to 1.00) for the composite of first cardiac hospitalization or cardiac death. Also, the effect of high-flux dialysis on ACM seemed to vary, depending on the duration of prior dialysis. This report presents secondary analyses to further explore the relationship between the flux intervention and the duration of dialysis with respect to various outcomes. The patients were stratified into a short-duration group and a long-duration group, on the basis of the mean duration of dialysis of 3.7 yr before randomization. In the subgroup that had been on dialysis for >3.7 yr, randomization to high-flux dialysis was associated with lower risks of ACM (RR, 0.68; 95% CI, 0.53 to 0.86; P = 0.001), the composite of first albumin level decrease or ACM (RR, 0.74; 95% CI, 0.60 to 0.91; P = 0.005), and cardiac deaths (RR, 0.63; 95% CI, 0.43 to 0.92; P = 0.016), compared with low-flux dialysis. No significant differences were observed in outcomes related to infection for either duration subgroup, however, and the trends for beneficial effects of high-flux dialysis on ACM rates were considerably weakened when the years of dialysis during the follow-up phase were combined with the prestudy years of dialysis in the analysis. For the subgroup of patients with <3.7 yr of dialysis before the study, assignment to high-flux dialysis had no significant effect on any of the examined clinical outcomes. These data suggest that high-flux dialysis might have a beneficial effect on cardiac outcomes. Because these results are derived from multiple statistical comparisons, however, they must be interpreted with caution. The subgroup results that demonstrate that patients with different durations of dialysis are affected differently by high-flux dialysis are interesting and require further study for confirmation.

Longitudinal progression trajectory of GFR among patients with CKD.

BACKGROUND The traditional paradigm of glomerular filtration rate (GFR) progression in patients with chronic kidney disease (CKD) is a steady nearly linear decline over time. We describe individual GFR progression trajectories over 12 years of follow-up in participants in the African American Study of Kidney Disease and Hypertension (AASK). STUDY DESIGN Longitudinal observational study. SETTING & PARTICIPANTS 846 AASK patients with at least 3 years of follow-up and 8 GFR estimates. MEASUREMENTS Longitudinal GFR estimates from creatinine-based equations. PREDICTORS Patient demographic and clinical features. OUTCOMES Probability of a nonlinear trajectory and probability of a period of nonprogression calculated for each patient from a Bayesian model of individual estimated GFR (eGFR) trajectories. RESULTS 352 (41.6%) patients showed a > 0.9 probability of having either a nonlinear trajectory or a prolonged nonprogression period; in 559 (66.1%), the probability was > 0.5. Baseline eGFR > 40 mL/min/1.73 m2 and urine protein-creatinine ratio < 0.22 g/g were associated with a higher likelihood of a nonprogression period. 74 patients (8.7%) had both a substantial period of stable or increasing eGFR and a substantial period of rapid eGFR decrease. LIMITATIONS Clinical trial population; absence of direct GFR measurements. CONCLUSIONS In contrast to the traditional paradigm of steady GFR progression over time, many patients with CKD have a nonlinear GFR trajectory or a prolonged period of nonprogression. These findings highlight the possibility that stable kidney disease progression can accelerate and, conversely, provide hope that CKD need not be relentlessly progressive. These results should encourage researchers to identify time-dependent factors associated with periods of nonprogression and other desirable trajectories.

Relationship between C-reactive protein, albumin, and cardiovascular disease in patients with chronic kidney disease

BACKGROUND C-Reactive protein (CRP) level is elevated in kidney failure and may be related to malnutrition and cardiovascular disease (CVD). Data are limited regarding relationships between CRP levels and glomerular filtration rate (GFR), nutritional indices, and CVD in patients with earlier stages of kidney disease. METHODS CRP was assayed from samples from the Modification of Diet in Renal Disease (MDRD) Study (n = 801). CRP distributions were compared between the MDRD Study and National Health and Nutrition Examination Survey (NHANES; 1999 to 2000). Associations between CRP level and GFR, nutritional indices, serum albumin levels, and CVD risk factors were examined in the MDRD Study. RESULTS Geometric means of CRP, adjusted for age and sex, were similar in NHANES (0.23 mg/dL) and the MDRD Study (0.22 mg/dL). In the MDRD Study, CRP level was related directly to measures of body fat and CVD risk factors, inversely with serum albumin level and energy intake, and unrelated to GFR. In multivariable analysis adjusting for other determinants of serum albumin level, high CRP level (>0.6 mg/dL) was associated with a 0.07-g/dL (0.7-g/L; 95% confidence interval [CI], 0.03 to 0.12) lower mean serum albumin level. After adjusting for traditional CVD risk factors, the odds of CVD were 1.73 (95% CI, 1.07 to 2.78) times greater in subjects with a high CRP level. CONCLUSION GFR level does not appear to influence CRP level in the earlier stages of chronic kidney disease. CRP levels are independently associated with serum albumin level and CVD prevalence. Inflammation may be involved in the pathophysiological state of malnutrition and CVD in the earlier stages of predominantly nondiabetic kidney disease.